{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "301dd4d3",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Alice: 555-1234, alice@gmail.com\n",
      "Bob: 555-5678, bob@qq.com\n"
     ]
    }
   ],
   "source": [
    "#使用dictionary结构，如下所示：\n",
    "friend_info = {}\n",
    "\n",
    "# 添加信息\n",
    "friend_info[\"Alice\"] = {\"phone\": \"555-1234\", \"email\": \"alice@gmail.com\"}\n",
    "friend_info[\"Bob\"] = {\"phone\": \"555-5678\", \"email\": \"bob@qq.com\"}\n",
    "\n",
    "# 输出信息\n",
    "for name, info in friend_info.items():\n",
    "    print(f\"{name}: {info['phone']}, {info['email']}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "c21e49cd",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "my face is two\n",
      "your face is four\n",
      "You win!\n"
     ]
    }
   ],
   "source": [
    "import random\n",
    "\n",
    "faces=['two','three','four','five','six','seven','eight','nine','ten','jack','queen','king','ace']\n",
    "my_face=random.choice(faces)\n",
    "print(f\"my face is {my_face}\")\n",
    "you_face=random.choice(faces)\n",
    "#you_face=input(‘Input your face:’)\n",
    "#print(\"\\n\")\n",
    "print(f\"your face is {you_face}\")\n",
    "\n",
    "if faces.index(my_face) > faces.index(you_face):\n",
    "    print(\"I win!\")\n",
    "elif faces.index(my_face) < faces.index(you_face):\n",
    "    print(\"You win!\")\n",
    "else:\n",
    "    print(\"It's a tie!\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "d1a29a72",
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[8, 4, 4, 6, 10, 10, 6, 3, 3, 5, 5, 9, 4, 9, 3, 2, 1, 2, 9, 1] 20\n",
      "[64, 16, 16, 36, 100, 100, 36, 9, 9, 25, 25, 81, 16, 81, 9, 4, 1, 4, 81, 1] 20\n"
     ]
    }
   ],
   "source": [
    "a_list = []\n",
    "b_list = []\n",
    "for i in range(20):\n",
    "    a_list.append(random.randint(1,10))\n",
    "print(a_list,len(a_list))\n",
    "\n",
    "for i in range(len(a_list)):\n",
    "    b_list.append(a_list[i]*a_list[i])\n",
    "print(b_list,len(b_list))\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "6a1ecd66",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Rock crushes scissors. Scissors cut paper. Paper covers rock.\n"
     ]
    }
   ],
   "source": [
    "import random\n",
    "\n",
    "choices = ['r', 'p', 's']\n",
    "\n",
    "while True:\n",
    "    print('Rock crushes scissors. Scissors cut paper. Paper covers rock.')\n",
    "    player = input('Do you want to be rock(r), paper(p) or scissors(s) (q to quit)?')\n",
    "    computer = random.choice(choices)\n",
    "\n",
    "    while player != 'q':\n",
    "        if player == computer:\n",
    "            print(\"It's a tie!\")\n",
    "            break\n",
    "        elif player == 'r' and computer == 's' or player == 'paper' and computer == 'rock' or player == 'scissors' and computer == 'paper':\n",
    "            print(\"You win!\")\n",
    "            break\n",
    "        else:\n",
    "            print(\"You lose!\")\n",
    "            break\n",
    "        break"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "e1c07049",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The black cat jumps 2 steps.\n"
     ]
    }
   ],
   "source": [
    "class Cat:\n",
    "    \n",
    "    def __init__(self, color):\n",
    "        self.color = color\n",
    "        \n",
    "    def jump(self, steps):\n",
    "        print(f\"The {self.color} cat jumps {steps} steps.\")\n",
    "        \n",
    "black_cat = Cat('black')\n",
    "black_cat.jump(2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "4fe9bd85",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "\n",
    "x_values = [10, 20, 40, 60, 100]\n",
    "y_values = [40, 20, 50, 70, 10]\n",
    "\n",
    "plt.scatter(x_values, y_values, color='blue')\n",
    "\n",
    "plt.title(\"Scatter Plot\")\n",
    "plt.xlabel(\"x-axis\")\n",
    "plt.ylabel(\"y-axis\")\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "5bad020c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "x = np.linspace(-2*np.pi, 2*np.pi, 1000)\n",
    "y = np.cos(x)\n",
    "\n",
    "plt.plot(x, y, color='blue')\n",
    "\n",
    "plt.title(\"Cosine Curve\")\n",
    "plt.xlabel(\"x-axis\")\n",
    "plt.ylabel(\"y-axis\")\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "0a3685c8",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.9.13"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}
